SoroushVahidi/scidocs huggingface dataset:
Boosting Response Aware Model-Based Collaborative Filtering
(2015)

Recommender systems are promising for providing personalized favorite services. Collaborative filtering (CF) technologies, making prediction of users' preference based on users' previous behaviors, have become one of the most successful techniques to build modern recommender systems. Several challenging issues occur in previously proposed CF methods: (1) most CF methods ignore users' response patterns and may yield biased parameter estimation and suboptimal performance; (2) some CF methods adopt heuristic weight settings, which lacks a systematical implementation; and (3) the multinomial mixture models may weaken the computational ability of matrix factorization for generating the data matrix, thus increasing the computational cost of training. To resolve these issues, we incorporate users' response models into the probabilistic matrix factorization (PMF), a popular matrix factorization CF model, to establish the response aware probabilistic matrix factorization (RAPMF) framework. More specifically, we make the assumption on the user response as a Bernoulli distribution which is parameterized by the rating scores for the observed ratings while as a step function for the unobserved ratings. Moreover, we speed up the algorithm by a mini-batch implementation and a crafting scheduling policy. Finally, we design different experimental protocols and conduct systematical empirical evaluation on both synthetic and real-world datasets to demonstrate the merits of the proposed RAPMF and its mini-batch implementation.
Interactive painterly stylization of images, videos and 3D animations
(2010)

We introduce a real-time system that converts images, video, or 3D animation sequences to artistic renderings in various painterly styles. The algorithm, which is entirely executed on the GPU, can efficiently process 512 resolution frames containing 60,000 individual strokes at over 30 fps. In order to exploit the parallel nature of GPUs, our algorithm determines the placement of strokes entirely from local pixel neighborhood information. The strokes are rendered as point sprites with textures. Temporal coherence is achieved by treating the brush strokes as particles and moving them based on optical flow. Our system renders high quality results while allowing the user interactive control over many stylistic parameters such as stroke size, texture and density.
Energy-traffic tradeoff cooperative offloading for mobile cloud computing
(2014)

This paper presents a quantitative study on the energy-traffic tradeoff problem from the perspective of entire Wireless Local Area Network (WLAN). We propose a novel Energy-Efficient Cooperative Offloading Model (E2COM) for energy-traffic tradeoff, which can ensure the fairness of energy consumption of mobile devices and reduce the computation repetition and eliminate the Internet data traffic redundancy through cooperative execution and sharing computation results. We design an Online Task Scheduling Algorithm (OTS) based on a pricing mechanism and Lyapunov optimization to address the problem without predicting future information on task arrivals, transmission rates and so on. OTS can achieve a desirable tradeoff between the energy consumption and Internet data traffic by appropriately setting the tradeoff coefficient. Simulation results demonstrate that E2COM is more efficient than no offloading and cloud offloading for a variety of typical mobile devices, applications and link qualities in WLAN.
Global Hypothesis Generation for 6D Object Pose Estimation
(2017)

This paper addresses the task of estimating the 6D-pose of a known 3D object from a single RGB-D image. Most modern approaches solve this task in three steps: i) compute local features, ii) generate a pool of pose-hypotheses, iii) select and refine a pose from the pool. This work focuses on the second step. While all existing approaches generate the hypotheses pool via local reasoning, e.g. RANSAC or Hough-Voting, we are the first to show that global reasoning is beneficial at this stage. In particular, we formulate a novel fully-connected Conditional Random Field (CRF) that outputs a very small number of pose-hypotheses. Despite the potential functions of the CRF being non-Gaussian, we give a new, efficient two-step optimization procedure, with some guarantees for optimality. We utilize our global hypotheses generation procedure to produce results that exceed state-of-the-art for the challenging Occluded Object Dataset.
Decision-Making in a Robotic Architecture for Autonomy
(2001)

This paper presents an overview of the intelligent decision-making capabilities of the CLARAty robotic architecture for autonomy. CLARAty is a two layered architecture where the top Decision Layer contains techniques for autonomously creating a plan of robot commands and the bottom Functional Layer provides standard robot capabilities that interface to system hardware. This paper focuses on the Decision Layer organization and capabilities. Specifically, the Decision Layer provides a framework for utilizing AI planning and executive techniques, which provide onboard, autonomous command generation and replanning for planetary rovers. The Decision Layer also provides a flexible interface to the Functional Layer, which can be tailored based on user preferences and domain features. This architecture is currently being tested on several JPL rovers.
Optimal estimation of the mean function based on discretely sampled functional data: Phase transition
(2011)

The problem of estimating the mean of random functions based on discretely sampled data arises naturally in functional data analysis. In this paper, we study optimal estimation of the mean function under both common and independent designs. Minimax rates of convergence are established and easily implementable rateoptimal estimators are introduced. The analysis reveals interesting and different phase transition phenomena in the two cases. Under the common design, the sampling frequency solely determines the optimal rate of convergence when it is relatively small and the sampling frequency has no effect on the optimal rate when it is large. On the other hand, under the independent design, the optimal rate of convergence is determined jointly by the sampling frequency and the number of curves when the sampling frequency is relatively small. When it is large, the sampling frequency has no effect on the optimal rate. Another interesting contrast between the two settings is that smoothing is necessary under the independent design, while, somewhat surprisingly, it is not essential under the common design.
CTrigger: exposing atomicity violation bugs from their hiding places
(2009)

Multicore hardware is making concurrent programs pervasive. Unfortunately, concurrent programs are prone to bugs. Among different types of concurrency bugs, atomicity violation bugs are common and important. Existing techniques to detect atomicity violation bugs suffer from one limitation: requiring bugs to manifest during monitored runs, which is an open problem in concurrent program testing.
 This paper makes two contributions. First, it studies the interleaving characteristics of the common practice in concurrent program testing (i.e., running a program over and over) to understand why atomicity violation bugs are hard to expose. Second, it proposes CTrigger to effectively and efficiently expose atomicity violation bugs in large programs. CTrigger focuses on a special type of interleavings (i.e., unserializable interleavings) that are inherently correlated to atomicity violation bugs, and uses trace analysis to systematically identify (likely) feasible unserializable interleavings with low occurrence-probability. CTrigger then uses minimum execution perturbation to exercise low-probability interleavings and expose difficult-to-catch atomicity violation.
 We evaluate CTrigger with real-world atomicity violation bugs from four sever/desktop applications (Apache, MySQL, Mozilla, and PBZIP2) and three SPLASH2 applications on 8-core machines. CTrigger efficiently exposes the tested bugs within 1--235 seconds, two to four orders of magnitude faster than stress testing. Without CTrigger, some of these bugs do not manifest even after 7 full days of stress testing. In addition, without deterministic replay support, once a bug is exposed, CTrigger can help programmers reliably reproduce it for diagnosis. Our tested bugs are reproduced by CTrigger mostly within 5 seconds, 300 to over 60000 times faster than stress testing.
Cross-Domain Visual Matching via Generalized Similarity Measure and Feature Learning
(2017)

Cross-domain visual data matching is one of the fundamental problems in many real-world vision tasks, e.g., matching persons across ID photos and surveillance videos. Conventional approaches to this problem usually involves two steps: i) projecting samples from different domains into a common space, and ii) computing (dis-)similarity in this space based on a certain distance. In this paper, we present a novel pairwise similarity measure that advances existing models by i) expanding traditional linear projections into affine transformations and ii) fusing affine Mahalanobis distance and Cosine similarity by a data-driven combination. Moreover, we unify our similarity measure with feature representation learning via deep convolutional neural networks. Specifically, we incorporate the similarity measure matrix into the deep architecture, enabling an end-to-end way of model optimization. We extensively evaluate our generalized similarity model in several challenging cross-domain matching tasks: person re-identification under different views and face verification over different modalities (i.e., faces from still images and videos, older and younger faces, and sketch and photo portraits). The experimental results demonstrate superior performance of our model over other state-of-the-art methods.
Design of Bidirectional DC–DC Resonant Converter for Vehicle-to-Grid (V2G) Applications
(2015)

In this paper, a detailed design procedure is presented for a bidirectional CLLLC-type resonant converter for a battery charging application. This converter is similar to an LLC-type resonant converter with an extra inductor and capacitor in the secondary side. Soft-switching can be ensured in all switches without additional snubber or clamp circuitry. Because of soft-switching in all switches, very high-frequency operation is possible; thus, the size of the magnetics and the filter capacitors can be made small. To reduce the size and cost of the converter, a CLLC-type resonant network is derived from the original CLLLC-type resonant network. First, in this paper, an equivalent model for the bidirectional converter is derived for the steady-state analysis. Then, the design methodology is presented for the CLLLC-type resonant converter. Design of this converter includes determining the transformer turns ratio, design of the magnetizing inductance based on zero-voltage switching condition, design of the resonant inductances and capacitances. Then, the CLLCtype resonant network is derived from the CLLLC-type resonant network. To validate the design procedure, a 3.5-kW converter was designed following the guidelines in the proposed methodology. A prototype was built and tested in the laboratory. Experimental results verified the design procedure presented.
Evolutionary Computation Meets Machine Learning: A Survey
(2011)

Evolutionary computation (EC) is a kind of optimization methodology inspired by the mechanisms of biological evolution and behaviors of living organisms. In the literature, the terminology evolutionary algorithms is frequently treated the same as EC. This article focuses on making a survey of researches based on using ML techniques to enhance EC algorithms. In the framework of an ML-technique enhanced-EC algorithm (MLEC), the main idea is that the EC algorithm has stored ample data about the search space, problem features, and population information during the iterative search process, thus the ML technique is helpful in analyzing these data for enhancing the search performance. The paper presents a survey of five categories: ML for population initialization, ML for fitness evaluation and selection, ML for population reproduction and variation, ML for algorithm adaptation, and ML for local search.
Obstruction-Free Synchronization: Double-Ended Queues as an Example
(2003)

We introduce obstruction-freedom, a new nonblocking property for shared data structure implementations. This property is strong enough to avoid the problems associated with locks, but it is weaker than previous nonblocking properties—specifically lock-freedom and wait-freedom— allowing greater flexibility in the design of efficient implementations. Obstruction-freedom admits substantially simpler implementations, and we believe that in practice it provides the benefits of wait-free and lock-free implementations. To illustrate the benefits of obstruction-freedom, we present two obstruction-free CAS-based implementations of double-ended queues (deques); the first is implemented on a linear array, the second on a circular array. To our knowledge, all previous nonblocking deque implementations are based on unrealistic assumptions about hardware support for synchronization, have restricted functionality, or have operations that interfere with operations at the opposite end of the deque even when the deque has many elements in it. Our obstruction-free implementations have none of these drawbacks, and thus suggest that it is much easier to design obstruction-free implementations than lock-free and waitfree ones. We also briefly discuss other obstruction-free data structures and operations that we have implemented.
Scheduling Multithreaded Computations by Work Stealing
(1999)

This paper studies the problem of efficiently schedulling fully strict (i.e., well-structured) multithreaded computations on parallel computers. A popular and practical method of scheduling this kind of dynamic MIMD-style computation is “work stealing,” in which processors needing work steal computational threads from other processors. In this paper, we give the first provably good work-stealing scheduler for multithreaded computations with dependencies.
Specifically, our analysis shows that the expected time to execute a fully strict computation on <italic>P</italic> processors using our work-stealing scheduler is <italic>T</italic><subscrpt>1</subscrpt>/<italic>P</italic> + <italic>O</italic>(<italic>T</italic><subscrpt><inline-equation> <f> ∞ </f></inline-equation></subscrpt>, where  <italic>T</italic><subscrpt>1</subscrpt> is the minimum serial execution time of the multithreaded computation and (<italic>T</italic><subscrpt><inline-equation> <f> ∞</f></inline-equation></subscrpt> is the minimum execution time with an infinite number of processors. Moreover, the space required by the execution is at most <italic>S</italic><subscrpt>1</subscrpt><italic>P</italic>, where  <italic>S</italic><subscrpt>1</subscrpt> is the minimum serial space requirement. We also show that the expected total communication of the algorithm is at most <italic>O</italic>(<italic>PT</italic><subscrpt><inline-equation> <f> ∞</f> </inline-equation></subscrpt>( 1 +  <italic>n<subscrpt>d</subscrpt></italic>)<italic>S</italic><subscrpt>max</subscrpt>), where <italic>S</italic><subscrpt>max</subscrpt> is the size of the largest activation record of any thread and  <italic>n<subscrpt>d</subscrpt></italic> is the maximum number of times that any thread synchronizes with its parent. This communication bound justifies the folk wisdom that work-stealing schedulers are more communication efficient than their work-sharing counterparts. All three of these bounds are existentially optimal to within a constant factor.
Linearizability: A Correctness Condition for Concurrent Objects
(1990)

A concurrent object is a data object shared by concurrent processes. Linearizability is a correctness condition for concurrent objects that exploits the semantics of abstract data types. It permits a high degree of concurrency, yet it permits programmers to specify and reason about concurrent objects using known techniques from the sequential domain. Linearizability provides the illusion that each operation applied by concurrent processes takes effect instantaneously at some point between its invocation and its response, implying that the meaning of a concurrent object's operations can be given by pre- and post-conditions. This paper defines linearizability, compares it to other correctness conditions, presents and demonstrates a method for proving the correctness of implementations, and shows how to reason about concurrent objects, given they are linearizable.
Introduction to Algorithms
(1989)

problems To understand the class of polynomial-time solvable proble ms, we must first have a formal notion of what a “problem” is. We define anbstract problemQ to be a binary relation on a set I of probleminstancesand a setS of problemsolutions. For example, an instance for SHORTEST-PATH is a triple consi sting of a graph and two vertices. A solution is a sequence of vertices in the g raph, with perhaps the empty sequence denoting that no path exists. The problem SHORTEST-PATH itself is the relation that associates each instance of a gra ph and two vertices with a shortest path in the graph that connects the two vertices. S ince shortest paths are not necessarily unique, a given problem instance may have mo r than one solution. This formulation of an abstract problem is more general than is required for our purposes. As we saw above, the theory of NP-completeness res tricts attention to decision problems : those having a yes/no solution. In this case, we can view an abstract decision problem as a function that maps the instan ce setI to the solution set {0, 1}. For example, a decision problem related to SHORTEST-PATH i s the problem PATH that we saw earlier. If i = 〈G,u, v,k〉 is an instance of the decision problem PATH, then PATH(i ) = 1 (yes) if a shortest path fromu to v has at mostk edges, and PATH (i ) = 0 (no) otherwise. Many abstract problems are not decision problems, but rather optimization problems , in which some value must be minimized or maximized. As we saw above, however, it is usual ly a simple matter to recast an optimization problem as a decision problem that is no harder. 1See Hopcroft and Ullman [156] or Lewis and Papadimitriou [20 4] for a thorough treatment of the Turing-machine model. 34.1 Polynomial time 973
An Axiomatic Basis for Computer Programming
(1968)

In this paper an attempt is made to explore the logical foundations of computer programming by use of techniques which were first applied in the study of geometry and have later been extended to other branches of mathematics. This involves the elucidation of sets of axioms and rules of inference which can be used in proofs of the properties of computer programs. Examples are given of such axioms and rules, and a formal proof of a simple theorem is displayed. Finally, it is argued that important advantage, both theoretical and practical, may follow from a pursuance of these topics.
Production of first and second generation biofuels : A comprehensive review
(2009)

Sustainable economic and industrial growth requires safe, sustainable resources of energy. For the future re-arrangement of a sustainable economy to biological raw materials, completely new approaches in research and development, production, and economy are necessary. The ‘first-generation’ biofuels appear unsustainable because of the potential stress that their production places on food commodities. For organic chemicals and materials these needs to follow a biorefinery model under environmentally sustainable conditions. Where these operate at present, their product range is largely limited to simple materials (i.e. cellulose, ethanol, and biofuels). Second generation biorefineries need to build on the need for sustainable chemical products through modern and proven green chemical technologies such as bioprocessing including pyrolysis, Fisher Tropsch, and other catalytic processes in order to make more complex molecules and materials on which a future sustainable society will be based. This review focus on cost effective technologies and the processes to convert biomass into useful liquid biofuels and bioproducts, with particular focus on some biorefinery concepts based on different feedstocks aiming at the integral utilization of these feedstocks for the production of value added chemicals. 2009 Elsevier Ltd. All rights reserved.
Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics.
(2010)

Electronics that are capable of intimate, non-invasive integration with the soft, curvilinear surfaces of biological tissues offer important opportunities for diagnosing and treating disease and for improving brain/machine interfaces. This article describes a material strategy for a type of bio-interfaced system that relies on ultrathin electronics supported by bioresorbable substrates of silk fibroin. Mounting such devices on tissue and then allowing the silk to dissolve and resorb initiates a spontaneous, conformal wrapping process driven by capillary forces at the biotic/abiotic interface. Specialized mesh designs and ultrathin forms for the electronics ensure minimal stresses on the tissue and highly conformal coverage, even for complex curvilinear surfaces, as confirmed by experimental and theoretical studies. In vivo, neural mapping experiments on feline animal models illustrate one mode of use for this class of technology. These concepts provide new capabilities for implantable and surgical devices.